The analysis of diffusion MRI data requires brain segmentation from separate anatomical images, which may be unavailable or cannot be accurately co-registered to diffusion images due to image distortions in diffusion data. Two state-of-the-art convolutional neural networks, U-Net and generative adversarial network (GAN), are employed to synthesize high-quality, distortion-matched T1w images directly from diffusion data, suitable for generating accurate cerebral cortical surfaces and volumetric segmentation for surface-based analysis of DTI metrics and tractography. The accuracy is quantitatively evaluated, and the systematical comparison shows that GAN-synthesized images are more visually appealing while U-Net-synthesized images achieve higher data consistency and segmentation accuracy.
This abstract and the presentation materials are available to members only; a login is required.